East-West Migration and Gender: Is there a Double Disadvantage vis-à-vis Stayers?

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From this document you will learn the answers to the following questions:

  • What category is included in the additional controls?

  • What is the specific type of variation in labor market outcome?

  • Which gender is more likely to suffer from migration?

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1 EastWest Migration and Gender: Is there a Double Disadvantage visàvis Stayers? Anzelika Zaiceva University of Bologna and IZA anzelika.zaiceva@unibo.it Higher School of Economics, Moscow December 13,

2 Outline Motivation and background Existing literature and this paper Data issues and methodology Descriptive evidence Econometric model Estimation results Additional issues Conclusions 2

3 Motivation Source: Statistisches Bundesamt, 2005 Fall of the Berlin Wall in 1989 and German reunification in 1990 an interesting experiment 7.5% of East Germans have moved West during (2 nd highest rates after Albania among excommunist countries) (Brücker and Trübswetter, 2004) The majority of EastWest German migrants are women How do migrant women perform in the western LM? 3

4 Background ExGDR: sex blindness. More than 80% of w.a. women participated in the LM, no (official) unemployment, wages were set centrally (with some adjustment at the firm level, Krueger and Pischke, 1995) During transition: participation declined, but is still high: 72% of w.a. women participated in the LM in May 2000 (Bonin and Euwals, 2005) Employment fell more than that of men: 58% of w.a. women were employed in 2000 (Hunt, 2002, Bonin and Euwals, 2005). Childcare facilities were reduced a lot, but the availability of childcare places is still higher in the East than in the West (Wrohlich, 2004) Fertility declined (Lechner, 2001) The gender wage gap has narrowed due to the exit from employment of low skilled women (Hunt, 2002) 4

5 Existing literature and this paper Theory: human capital migration theory (Sjaastad, 1962) vs. family migration models ( tied movers, Mincer, 1978) and family investment models (Baker and Benjamin, 1997, Duleep and Dowhan, 2002) Empirics: immigrant women experience a double disadvantage wrt. several labor market outcomes, compared to immigrant males and native women (Adsera and Chiswick, 2007, Bevelander and Groeneveld, 2007, De Jong and Madamba, 2001, Raijman and Semyonov, 1997, Boyd, 1984) Related literature: family migration (e.g. Boyle et al., 2002) and female migrants assimilation (e.g. Blau and Kah, 2005) This paper: How do female migrants from a transition economy perform (in relative terms) after they move West? That is: whether female migrants after they move gain or suffer from migration relatively more than males 5

6 Data and methodology GSOEP: Eastern sample, Sample size: from 18,126 to 8,984 observations, 4251,169 belong to the migration group (actual and potential) Sample selection: Exclude: younger than 18 and older than 65, return migrants, in fulltime education and military service, those for whom do not observe before and after periods, drop outliers. Deflate incomes. Four outcomes: annual income, employment, hours worked and hourly earnings Descriptive, no structural model First calculate unadjusted differences in means, then control for observable and timeinvariant unobservable confounders (OLS and FE) 6

7 Descriptive evidence: differences in means Log annual income: Before After AfterBefore Males (1) Females (2) Males (3) Females (4) Males (5) Females (6) Stayers *** (0.010) (0.011) (0.011) (0.011) (0.014) (0.016) Migrants *** (0.054) (0.050) (0.035) (0.052) (0.062) (0.073) MS 0.117*** 0.098*** 0.385*** 0.156*** 0.502*** (0.043) (0.044) (0.041) (0.043) (0.064) (0.075) Difference 0.560*** (0.099) Employment: DIFF=0.115** (0.045) Log hours per week: DIFF=0.272*** (0.043) Log hourly earnings: DIFF=0.129 (0.087) 7

8 Regression framework Y = β ( F M A) + β ( F M ) + β ( F A) + β ( M A) + it, 1 i i t 2 i i 3 i t 4 i t + β M + β F + β A + δx + ε 5 i 6 i 7 t i, t i, t where: F i is female, M i is migrant, A t stands for the period after, X it are controls, β 1 captures all variation in labor market outcome specific to migrants (relative to stayers) females (relative to males) in the years after migration (relative to before) β 1 shows whether female migrants after move suffer from migration more than males, i.e. an additional female migrantspecific effect Controls: age and its square, marital status, number of children, education categories (university, general schooling, other vocational; reference: apprenticeship), blue collar, public sector, others hh income (in LS eqs), year and region dummies 8

9 Main estimation results OLS (1) OLS (2) OLS (3) OLS (4) FE (5) Annual income 0.443**** 0.474*** 0.456*** 0.513*** 0.239*** Obs. (0.095) [13119] (0.082) [13119] (0.097) [9244] (0.111) [12854] (0.074) [13119] R Employment 0.106** *** 0.082* Obs. (0.046) [18126] (0.045) [15990] (0.053) [11464] (0.046) [18126] (0.049) [15990] R Controls Controls at t=0 Earnings in 1991 Year dummies Region dummies te: standard errors in parenthesis. *** signi.cant at 1% level, ** signi.cant at 5% level, * signi.cant at 10% level.. Additional controls include age and its square, marital status, number of children less than 14 years old, education, bluecollar dummies, others income (in empl. eq.), year and region fixed effects. In fixed effects (FE) estimation timeinvariant covariates are dropped. 9

10 Main estimation results (cont d) OLS (1) OLS (2) OLS (3) OLS (4) FE (5) Weekly hours 0.261*** 0.220*** 0.266*** 0.233*** 0.246*** (0.041) (0.041) (0.053) (0.047) (0.056) Obs. [13729] [12475] [9729] [12130] [12475] R Hourly earnings Obs. (0.087) [12461] (0.072) [12279] (0.086) [11088] (0.071) [12279] R Controls Controls at t=0 Earnings in 1991 Year dummies Region dummies te: standard errors in parenthesis. *** signi.cant at 1% level, ** signi.cant at 5% level, * signi.cant at 10% level.. Additional controls include age and its square, marital status, number of children less than 14 years old, education, bluecollar dummies, others income, year and region fixed effects. In fixed effects (FE) estimation timeinvariant covariates are dropped 10

11 Other results: annual income OLS (1) OLS (2) OLS (3) OLS (4) FE (5) 0.443**** 0.474*** 0.456*** 0.513*** 0.239*** (0.095) (0.082) (0.097) (0.111) (0.074) M*A *** 0.983*** 0.342*** (0.544) (0.409) (0.042) (0.208) (0.119) F*A *** (0.019) (0.016) (0.016) (0.019) (0.012) F*M (0.071) (0.059) (0.057) (0.078) A 0.127** * (0.057) (0.050) (0.053) (0.063) (0.035) M (0.047) (0.039) (0.036) (0.057) F 0.225*** 0.310*** 0.240*** 0.363*** (0.013) (0.012) (0.011) (0.013) Controls Controls at t=0 Earnings in 1991 Year dummies Region dummies 11

12 Other results: employment OLS (1) OLS (2) OLS (3) OLS (4) FE (5) 0.106** *** 0.082* (0.046) (0.045) (0.053) (0.046) (0.049) M*A 0.431*** (0.040) (0.075) 0.094*** (0.030) 0.520*** (0.041) (0.116) F*A 0.035*** 0.030*** *** 0.019** (0.012) (0.011) (0.011) (0.011) (0.009) F*M *** (0.037) (0.036) (0.037) (0.037) A 0.107*** 0.100*** 0.060* 0.112*** 0.077*** (0.028) (0.027) (0.034) (0.027) (0.026) M 0.075*** 0.047* 0.072*** 0.084*** (0.027) (0.026) (0.026) (0.027) F 0.137*** (0.008) (0.008) (0.008) 0.100*** (0.008) Controls Controls at t=0 Earnings in 1991 Year dummies Region dummies 12

13 Other results: weekly hours OLS (1) OLS (2) OLS (3) OLS (4) FE (5) 0.261*** 0.261*** 0.266*** 0.233*** 0.246*** (0.041) (0.041) (0.053) (0.047) (0.056) M*A 0.329*** (0.038) 0.219*** (0.041) (0.053) 0.266*** (0.044) (0.105) F*A 0.040*** 0.043*** 0.020** 0.025*** 0.032*** (0.010) (0.010) (0.010) (0.009) (0.009) F*M (0.025) (0.026) (0.034) (0.027) A (0.024) (0.024) (0.029) (0.022) (0.024) M 0.036* 0.046** 0.045* (0.020) (0.021) (0.025) (0.021) F 0.130*** (0.007) 0.145*** (0.007) 0.145*** (0.008) 0.129*** (0.007) Controls Controls at t=0 Earnings in 1991 Year dummies Region dummies 13

14 Other results: hourly earnings OLS (1) OLS (2) OLS (3) OLS (4) FE (5) (0.087) (0.072) (0.086) (0.071) M*A (0.390) 0.302*** (0.052) 0.242*** (0.076) 0.345*** (0.151) F*A 0.044** 0.061*** 0.058*** 0.044*** (0.018) (0.016) (0.017) (0.013) F*M (0.069) (0.054) (0.066) A 0.153*** (0.051) (0.043) (0.048) (0.039) M (0.045) (0.039) (0.048) F 0.073*** (0.013) 0.116*** (0.012) 0.157*** (0.013) Controls Controls at t=0 Earnings in 1991 Year dummies Region dummies 14

15 What have we learned so far? There exist an additional effect of being both a female and a migrant (on top of the effects for all migrants and all females) for some LM outcomes Compared to stayers and male migrants, EastWest migrant women in Germany after migration neither experience a drop in relative employment, nor earn lower relative wages. But they work relatively less hours (income effect seems to dominate the substitution effect) and have a lower relative annual income 15

16 Heterogeneity of the effect Annual income Hours per week OLS FE OLS FE Married 0.669*** 0.150** 0.227*** 0.265*** (0.096) (0.076) (0.054) (0.070) Married with kids 0.910*** (0.119) 0.192*** (0.101) 0.318*** (0.060) 0.142*** (0.073) Married before 0.453*** (0.106) 0.157** (0.082) 0.160*** (0.064) 0.265*** (0.087) Kids before 0.615*** (0.116) 0.227*** (0.090) 0.243*** (0.066) 0.235*** (0.064) Single (0.143) (0.132) (0.074) (0.065) Worked before 0.392*** (0.090) 0.302*** (0.079) 0.227*** (0.048) 0.266*** (0.056) te: robust standard errors are given in parenthesis, sample size in brackets. *** significant at 1% level, ** significant at 5% level, * signi.ficant at 10% level. Only the coefficient on the thirdlevel interaction in equation (1) is reported. Rows define the subpopulations for which the model is estimated. Additional controls in OLS include age and its square, university degree, general schooling degree, vocational training (referenceapprenticeship), bluecollar worker, public sector employee, year and region fixed effects (as well as others income in the equation for weekly hours). In fixed effects estimation (FE) time invariant covariates are dropped. 16

17 The effect of additional interactions Annual income Hours per week OLS FE OLS FE Married* 0.425*** (0.101) 0.319** (0.119) 0.171*** (0.063) 0.194*** (0.084) Kids* 0.526*** (0.099) 0.260*** (0.111) 0.184*** (0.060) (0.074) Married before* *** (0.096) (0.116) (0.045) (0.083) Time for childcare* 0.081*** (0.019) 0.072*** (0.022) 0.039*** (0.008) 0.037*** (0.010) Others income* *** *** *** ** ( ) ( ) ( ) ( ) te: robust standard errors are given in parenthesis, sample size in brackets. *** significant at 1% level, ** significant at 5% level, * significant at 10% level. "Hours for childcare" are reported hours spent per weekday on childcare; "others income" stands for monthly income of other members of the household. Controls include age and its square, married, number of kids less than 14 years old, university degree, general schooling degree, vocational training (referenceapprenticeship), bluecollar worker, public sector employee, year and region fixed effects (as well as others. income in the equation for weekly hours). In fixed effects estimation (FE) time invariant covariates are dropped. 17

18 The effect in western Germany OLS FE OLS FE Annual income Employment (0.067) 0.247** (0.119) (0.030) (0.064) R Obs Weekly hours Hourly earnings 0.069* (0.038) 0.212*** (0.072) (0.053) (0.085) R Obs te: robust standard errors are given in parenthesis. *** significant at 1%, ** significant at 5%, * significant at 10% level. Controls include age and its square, married, number of kids less than 14 years old, university degree, general schooling degree, vocational training (reference apprenticeship), bluecollar worker, public sector employee (not for employment equation), year and region fixed effects (as well as others income in equations for employment, hours and hourly wages). In fixed effects estimation (FE) time invariant covariates are dropped. 18

19 Robustness Results are robust to changes in specification and in the sample : Controlling for household size and household income Controlling for occupations Retaining commuters in stayers group 19

20 Main conclusions Compared to stayers and male migrants, EastWest migrant women in Germany after migration neither experience a drop in relative employment, nor earn lower relative wages. But they work relatively less hours (income effect seems to dominate the substitution effect) and have a lower relative annual income This effect is heterogenous across different demographic groups Fertility and marriage? The effect holds for married and married with children and is not present for single female migrants Endogeneity? The effect is present after having conditioned on premigration characteristics Moving out of unemployment? The effect is present after having conditioned on working before migration 20

21 Main conclusions (cont d) Why negative relative effect? Choice or disadvantage? Preferences or demand? difficult to answer Partner s income effect and fertility effect: having a husband in the West, spending time for childcare and a higher income of other hh members contribute to the explanation of this negative effect These results also seem to suggest that female migrants in the West substitute some market work with home production, in particular, childcare Reduced hours involuntary choice: t enough fulltime jobs, reduced availability of childcare. But also: availability of parttime jobs in the West The negative effect is transitionspecific and is not present for the within West migrant women A combination of supply and demand factors 21

22 Further research Matching in the first stage to construct T and C groups (like in Difindif models). But: selection on observables Explore further demand constraint: desired working hours 22

23 СПАСИБО! 23

Anzelika Zaiceva y. European University Institute and IZA. October 2006 PRELIMINARY. Abstract

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